Computational Linguistics

Teacher(s)

Dupont Pierre; Dupont Pierre (compensates Fairon Cédrick); Fairon Cédrick;

Language

English

Main themes

• Basics in phonology, morphology, syntax and semantics
• Linguistic resources
• Part-of-speech tagging
• Statistical language modeling (N-grams and Hidden Markov Models)
• Robust parsing techniques, probabilistic context-free grammars
• Linguistics engineering applications such as spell or syntax checking software, POS tagging, document indexing and retrieval, text categorization

Aims

At the end of this learning unit, the student is able to :
1	Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes: INFO1.1-3 INFO2.3-4 INFO5.3-5 INFO6.1, INFO6.4 Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes: SINF1.M4 SINF2.3-4 SINF5.3-5 SINF6.1, SINF6.4 Students completing successfully this course should be able to describe the fundamental concepts of natural language modeling master the methodology of using linguistic resources (corpora, dictionaries, semantic networks, etc) and make an argued choice between various linguistic resources apply in a relevant way statistical language modeling techniques develop linguistic engineering applications Students will have developed skills and operational methodology. In particular, they have developed their ability to integrate a multidisciplinary approach to the edge between computer science and linguistics, using wisely the terminology and tools of one or the other discipline, manage the time available to complete mini-projects, manipulate and exploit large amounts of data.

Content

• Various levels of linguistic analysis
• (Automated) corpus processing: formating, tokenization, data tagging
• Probabilistic language models: N-grams, HMMs
• Part-of-Speech Tagging
• (Probabilistic) Context-Free Grammars: parameter estimation and parsing algorithms
• Introduction to Machine Translation
• Introduction to Deep Learning
• Typical linguistic applications such as automated completion, POS taggers, parsing or machine translation.

Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

• Lectures
• Practical projects implemented in Python.

By default, lectures can be followed face to face in the auditorium announced in the official schedule. Depending on the number of registered students and the evolution of the sanitary situation, students will be able to follow the lectures as well remotely on Teams.
Practical projects are submitted on line and evaluated on the Inginious platform.

Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

The projects are worth 30 % of the final grade, 70 % for the final exam (closed-book).
The projects cannot be implemented again in second session.
The project grades are fixed at the end of the semester and included as such in the global score for the second session.
The final exam is, by default, a written exam (on paper or, when appropriate, on a computer).
These evaluation rules are subject to possible updates due to the sanitary situation. In particular, the relative weights between the projects and the final exam could be adapted.  Such possible updates would be notified to the students by a general announcement  posted on the Moodle site of this course.

Online resources

http://moodleucl.uclouvain.be/course/view.php?id=7865

Bibliography

One recommended textbook - un ouvrage conseillé :

• Speech and Language Processing (2nd Edition), D. Jurafsky and J.H. Martin, Prentice Hall, 2009.

Teaching materials

• Les supports obligatoires sont constitués de l'ensemble des documents (transparents des cours magistraux, énoncés des travaux pratiques, compléments, ...) disponibles depuis le site Moodle du cours.
• Required teaching material include all documents (lecture slides, project assignments, complements, ...) available from the Moodle website for this course.

Faculty or entity

INFO